Debloating Software through Piece-Wise Compilation and Loading
Not on the map. Why?
Abstract (arXiv)
Programs are bloated. Our study shows that only 5% of libc is used on average across the Ubuntu Desktop environment (2016 programs); the heaviest user, vlc media player, only needed 18%. In this paper: (1) We present a debloating framework built on a compiler toolchain that can successfully debloat programs (shared/static libraries and executables). Our solution can successfully compile and load most libraries on Ubuntu Desktop 16.04. (2) We demonstrate the elimination of over 79% of code from coreutils and 86% of code from SPEC CPU 2006 benchmark programs without affecting functionality. We show that even complex programs such as Firefox and curl can be debloated without a need to recompile. (3) We demonstrate the security impact of debloating by eliminating over 71% of reusable code gadgets from the coreutils suite and show that unused code that contains real-world vulnerabilities can also be successfully eliminated without adverse effects on the program. (4) We incur a low load time overhead.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Feb 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 0; 2026: 4.
6 papers.
- 2026SkillReducer: Optimizing LLM Agent Skills for Token Efficiencycited by 33
- 2023Exploring the Limits of ChatGPT in Software Security Applicationscited by 6
- 2022Machine Learning Systems are Bloated and Vulnerablecited by 2
- 2026Rising From the Ashes: How Agentic AI is Unblocking Challenges in Cybersecuritycited by 0
- 2026TRIM: Reducing AI-Generated CodeSlop via Agent Trajectory Minimizationcited by 0
- 2026Code That Works, Environments That Don't: Measuring Environment Reproducibility in AI-Generated Softwarecited by 0
- 2026Code That Works, Environments That Don't: Measuring Environment Reproducibility in AI-Generated Software30 Sep 2026
- 2026TRIM: Reducing AI-Generated CodeSlop via Agent Trajectory Minimization20 Jul 2026
- 2026Rising From the Ashes: How Agentic AI is Unblocking Challenges in Cybersecurity22 Jun 2026
- 2026SkillReducer: Optimizing LLM Agent Skills for Token Efficiency31 Mar 2026
- 2023Exploring the Limits of ChatGPT in Software Security Applications8 Dec 2023
- 2022Machine Learning Systems are Bloated and Vulnerable16 Dec 2022